PEB-TAXO: Projecting Entities as Boxes for Taxonomy Expansion

• Published in: Neural processing letters 2024-03, Vol.56 (2), p.102, Article 102
• Main Authors: Zhang, Yuhang, Qin, Jiwei, Feng, Chongren
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Boxes; Complex Systems; Computational Intelligence; Computer Science; Hierarchies; Knowledge; Regularization; Researchers; Semantics; Taxonomy
• ISSN: 1573-773X; 1370-4621; 1573-773X
• DOI: 10.1007/s11063-024-11575-3

As domain knowledge evolves, new concepts (entities) continuously emerge, leading to a decrease in the coverage of existing taxonomies with hierarchical structures, thus necessitating the continual expansion of these taxonomies to include new concepts. Due to the relationships (“contain”, “disjoint”, and “intersect”) between the boxes, which can effectively represent asymmetric hierarchies, box embeddings have been successfully applied in taxonomy expansion. However, existing models that use box embeddings for taxonomy expansion have the following shortcomings: (1) the size of the boxes is not restricted, and the model produces meaningless boxes; (2) the model does not fully utilize the geometric information of the boxes. To address the above shortcomings, this paper proposes a taxonomy expansion model based on projecting entities as boxes: PEB-TAXO. Firstly, PEB-TAXO employs modified L1 regularization to constrain the box sizes in all dimensions, pushing the box sizes towards the preset minimum, thereby avoiding the generation of meaningless boxes by the model. Secondly, the model utilizes a box inclusion inference method: it infers the relationship between two entities through the relationship between two boxes in geometric space, thus fully exploiting the geometric information of the boxes for more accurate inferences. Finally, we conducted extensive experiments on two public datasets and verified that PEB-TAXO greatly improves performance over mainstream taxonomy expansion methods.